Abstract not found

Richly interlinked, machine-understandable data constitutes the basis for the Semantic Web. Annotating web documents is one of the major techniques for creating metadata on the Web. However, annotation tools so far are restricted in their capabilities of providing richly interlinked and truely machine-understandable data. They basically allow the user to annotate with plain text according to a template structure, such as Dublin Core. We here present CREAM (Creating RElational, Annotationbased Metadata), a framework for an annotation environment that allows to construct relational metadata, i.e. metadata that comprises class instances and relationship instances. These instances are not based on a fix structure, but on a domain ontology. We discuss some of the requirements one has to meet when developing such a framework, e.g. the integration of a metadata crawler, inference services, document management and information extraction, and describe its implementation, viz. Ont-O-Mat a component-based, ontology-driven annotation tool.

Abstract. With rising importance of knowledge interchange, many industrial and academic applications have adopted ontologies as their conceptual backbone. However, industrial and academic environments are very dynamic, thus inducing changes to application requirements. To fulfill these changes, often the underlying ontology must be evolved as well. As ontologies grow in size, the complexity of change management increases, thus requiring a wellstructured ontology evolution process. In this paper we identify a possible sixphase evolution process and focus on providing the user with capabilities to control and customize it. We introduce the concept of an evolution strategy encapsulating policy for evolution with respect to user’s requirements. 1

We present a method and a tool, OntoLearn, aimed at the extraction of domain ontologies from web sites, and more generally from documents shared among the members of virtual organizations. OntoLearn first extracts a domain terminology from available documents. Then, complex domain terms are semantically interpreted and arranged in a hierarchical fashion. Finally, a general purpose ontology, i.e. WordNet, is trimmed and enriched with the detected domain concepts. The major novel aspect of this approach is semantic interpretation, that is, the association of a complex concept with a complex term. This involves finding the appropriate WordNet concept for each word of a terminological string and the appropriate conceptual relations that hold among the concept components. Semantic interpretation is based on a new WSD algorithm, called structural semantic interconnections.

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.

Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. 1 A Motivating Example: the Semantic Web The current World-Wide Web has well over 1.5 billion pages [2], but the vast majority of them are in human-readable format only (e.g., HTML). As Work done while the author was at the University of Washington, Seattle 2 AnHai Doan et al. a consequence software agents (softbots) cannot understand and process this information, and much of the potential of the Web has so far remained untapped. In response, researchers have created the vision of the Semantic Web [5], where data has structure and ontologies describe the semantics of the data. When data is marked up using ontologies, softbots can better understand the semantics and therefore more intelligently locate and integrate data for a wide variety of tasks. The following example illustrates the vision of the Semantic Web. Example 1. Suppose you want to fi

The vision of the Semantic Web can only be realized through proliferation of well-known ontologies describing different domains. To enable interoperability in the Semantic Web, it will be necessary to break these ontologies down into smaller, well-focused units that may be reused. Currently, three problems arise in that scenario. Firstly, it is difficult to locate ontologies to be reused, thus leading to many ontologies modeling the same thing. Secondly, current tools do not provide means for reusing existing ontologies in new ontologies. Finally, ontologies are rarely static, but are being adapted to changing requirements. Hence, an infrastructure for management of ontology changes, taking into account dependencies between ontologies is needed. In this paper we present such an infrastructure addressing the aforementioned problems.

for automated ontology learning extracts relevant domain terms from a corpus of text, relates them to appropriate concepts in a general-purpose ontology, and detects taxonomic and other semantic relations among the concepts. The authors used it to automatically translate multiword terms from English to Italian.

The Semantic Web is intended to enable machine processability of web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that describe the formal semantics of Web contents. We present a novel, integrated and automated approach for migrating dam-intensive Web applications into the Semantic Web. This approach can be applied to a broad range of today's business Web sites.

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.